Article, 2024

Why traditional firms from the same industry reject digital transformation: Structural constraints of perception and attention

Long Range Planning, ISSN 0024-6301, Volume 57, 2, 10.1016/j.lrp.2024.102426

Contributors

Fernandes E. 0000-0002-1266-8345 [1] Burcharth A. 0000-0002-9921-5188 (Corresponding author) [1] [2]

Affiliations

  1. [1] Fundação Dom Cabral
  2. [NORA names: Brazil; America, South];
  3. [2] Aarhus University
  4. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

We explain why some traditional companies fail to sense new digital technologies when facing an identical scenario of digital transformation. Our objective is to investigate situations where discontinuous changes steaming from digital transformation are actively rejected, in the sense that they are not perceived as a strategic issue, i.e., a threat or opportunity. We draw on a mixed-method research design comprising two sequential studies. The first study is based on Delphi's Technique, which uses a panel of specialists to build the most likely future scenarios in the medium term for the language education industry. The second one is a qualitative comparative study with eleven traditional firms. Their senior executives were first asked for their spontaneous sensing of emerging technologies and later asked to provide their assessment of the most likely future scenarios. Our contribution lies in developing a conceptual model that proposes a structural “schema-driven” explanation of why firm-level structures – concrete, contextual and knowledge – can hinder perception and attention. Active rejection is prompted not by the absence of attentional structures, but by their specific attributes. This expands the dominant ontology of issues, asserting their existence independently of an organization's epistemological experience, and adds to the theoretical understanding regarding the constraints of the sensing dynamic capability in digital transformation.

Keywords

Attention-based view, Cognition, Digital transformation, Dynamic capabilities, Sensing capability

Data Provider: Elsevier